Electrical energy storage devices (e.g., batteries, fuel cells, ultracapacitors, etc.) have become important subsystems for many military, space and commercial applications. Consequently, in situ diagnostics for accurate state-of-health estimations have also gained significant interest. For many applications, however, it is insufficient to monitor simple parameters such as voltage, current, and temperature to gauge the remaining capacity of the energy storage device. Knowledge of impedance and power capability also may be necessary for an accurate state-of-health estimation. An important component of in situ impedance monitoring is rapid measurements that minimally perturb the energy storage device.
Advanced techniques for real-time assessment of the impedance spectra for energy storage devices have been proposed. Many of these techniques can be implemented in an embedded device and periodically query the energy storage device to determine its state-of-health. For example, it has been shown that a shift in the impedance spectra of battery technologies strongly correlates to the corresponding pulse resistance and power capability.
One technique, referred to herein as Impedance Noise Identification (INI), disclosed in U.S. Pat. No. 7,675,293 to Christophersen et al., uses a random signal excitation covering a frequency range of interest and monitors a response. The input and response signals may be cross-correlated, normalized by an auto-correlated input signal, and then averaged and converted to the frequency domain through Fast Fourier Transforms. INI can be implemented on an embedded system and yield high-resolution data.
Another technique, referred to herein as Compensated Synchronous Detection (CSD), disclosed in U.S. Pat. No. 7,395,163 to Morrison et al., uses a sum-of-sines (SOS) input signal that adequately covers a frequency range of interest. The magnitude and phase at each frequency of the response signal is initially determined through synchronous detection. However, these data may be tainted by cross-talk error, so the response signal is reassembled with all the frequencies except the one of interest, and then subtracted from the original response signal and synchronously detected again. Generally, CSD may be more rapid than INI, but it may need three periods of the lowest frequency, and trades off resolution for speed of measurement.
Yet another technique, referred to herein as Fast Summation Transformation (FST), disclosed in U.S. patent application Ser. No. 12/217,013 to Morrison et al., also uses an SOS input signal that covers a frequency range of interest. However, to eliminate the cross-talk error, the frequency is increased in octave harmonic steps. Thus, no compensation is required and the response signal can simply be rectified relative to the sine and the cosine to establish the impedance spectra. Some attributes of FST are that it only requires a time record of acquired data covering one period of the lowest frequency, and the data processing algorithm is very simple. However, with FST the resolution in frequencies cannot be any finer than octave steps.
Battery performance degrades as a function of age, but the mechanisms responsible for failure are complex. Various different commercial fields (auto, military, space, etc.) are looking for ways to accurately assess a battery's state-of-health and estimate remaining life with a high degree of confidence. Many systems may require this assessment to be made in situ while minimally perturbing the battery so as not to create additional aging effects due to the measurement. However, in situ impedance monitoring presents a significant challenge. The difficulty is to accurately determine impedance in real time with minimal impact on battery life.
An accurate state-of-health meter would allow for a more efficient use of battery systems in various applications, which can have a significant economic benefit since good batteries are presently being discarded at regular intervals to ensure continuous operation. If a battery status monitor successfully projects life with a high degree of confidence, users will be able to keep batteries in operation longer, and also identify bad batteries much earlier in life as well.